An efficient family of robust-type estimators for the population variance in simple and stratified random sampling

Zaman, Tolga; Bulut, Hasan

doi:10.1080/03610926.2021.1955388

Cited by 23 publications

(15 citation statements)

References 23 publications

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“…We remark that the expression for the MSE of the proposed class of robust estimators is in the same form as expression for the MSE presented in (16)  .…”

Section: Mse T S B S a S A B S S N S B S A S J MCD And Mvementioning

confidence: 94%

“…The first estimators are as given in (15). The MSE of these estimators is given in (16). We obtained the MSE values of proposed classical estimators in (15), the unbiased estimator, Isaki estimator in (2), Singh et al estimator in (4), the regression estimator in (7), Upadhyaya and Singh estimator in (9), and Kadilar and Cingi estimators in (11).…”

Section: Applicationsmentioning

confidence: 99%

“…Bulut and Zaman [15] presented the ratio-type estimators utilizing MCD estimates. Zaman and Bulut [16] provided the ratio estimators for population variance considering MCD and MVE robust covariance estimates, both simple and stratified random sampling. Zaman et al [17] presented the robust regression-ratio-type estimators of the mean utilizing two auxiliary variables.…”

Section: Introductionmentioning

confidence: 99%

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A new class of robust ratio estimators for finite population variance

Zaman

Bulut

2022

Scientia Iranica

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It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the mean square error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.

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“…We remark that the expression for the MSE of the proposed class of robust estimators is in the same form as expression for the MSE presented in (16)  .…”

Section: Mse T S B S a S A B S S N S B S A S J MCD And Mvementioning

confidence: 94%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new class of robust ratio estimators for finite population variance

Zaman

Bulut

2022

Scientia Iranica

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“…Bulut and Zaman 32 enhanced the efficiency of ratio estimators by adopting the minimum covariance determinant technique. Zaman and Bulut 33 have further improved the efficacy of the estimators suggested by Bulut and Zaman 32 under stratified sampling. Abid et al 34 proposed an improved and robust class of ratio estimators using various robust measures of dispersion.…”

Section: Introductionmentioning

confidence: 99%

“…Ali et al 36 suggested some improved robust regression estimators for the mean estimation of the sensitive variables. Zaman and Bulut 37 suggested an efficient family of robust estimators under simple and stratified sampling techniques.…”

Section: Introductionmentioning

confidence: 99%

On enhanced exponential‐cum‐ratio estimators using robust measures of location

Gulzar

Latif

Abid

et al. 2021

Concurrency and Computation

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Many studies are mainly concerned with the estimation of finite population mean and the well-known preferences for it are ratio estimators. This article offers a remedy for improvement in the estimation of a study variable, based on supplementary information related to dual auxiliary variables in the context of simple random sampling without replacement scheme. We proposed two new general classes of exponential-cum-ratio estimators to estimate a finite population mean utilizing dual auxiliary variables with suitable combinations of the conventional and nonconventional measures. The expression for the mean square error (MSE) and theoretical conditions for proposed classes have been obtained for evaluation purposes. The performance of the proposed classes has been compared with existing estimators in terms of MSE. It is revealed that proposed estimators are more efficient than usual and existing estimators considered in this article. The simulation and robustness studies are also part of this article. Moreover, a variety of real data sets is also considered for an empirical study to support the theoretical results.

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Impact of information security awareness on information security compliance of academic library staff in Türkiye

Kavak

2024

The Journal of Academic Librarianship

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An efficient family of robust-type estimators for the population variance in simple and stratified random sampling

Cited by 23 publications

References 23 publications

A new class of robust ratio estimators for finite population variance

A new class of robust ratio estimators for finite population variance

On enhanced exponential‐cum‐ratio estimators using robust measures of location

Impact of information security awareness on information security compliance of academic library staff in Türkiye

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